As the Internet continues to exponentially expand, the role of search engines has dramatically increased. The sheer volume of data has quickly become impossible for a human user to manipulate on his or her own. Thus, the use of search engine technology has become a vital tool in the useful operation of the Internet.
A primary economical and functional objective of a search engine is to increase the relevancy of search results for a given query. Primarily displaying the most relevant search results first, followed by search results in descending relevancy, can maximize relevancy. In this regard, a user can quickly identify the most relevant content quickly, ensuring high user retention for a search engine.
Currently, there are two main approaches to maximizing search result relevancy. The original approach is fully automatic with features extracted from the page and its links. However, this approach has become outdated in view of the new face of content added to the Internet. A new approach, used by Google, Microsoft, Yahoo, and recent startups, is to use supervised machine learning methods. Due to the nature of these methods, they require training, which implies training data, hence, the name ‘supervised’. The training data may consist of a large number of queries and the corresponding search results. These queries and results can be selected automatically to maximize various objectives such as a random and diverse set of queries but can also be created by humans. The most important part of the human involvement is in grading or assigning a numerical score to each search result so that supervised machine learning methods can learn to optimize.
Concurrent with the growth of computing technologies is the growth of online competitions, including online games. There are known techniques for harnessing user productivity through competitions. By way of example, there are question and answer applications, e.g. Yahoo! Answers, where people get points for answering questions. Users achieve points and online status by answering questions and concurrently, the content of the system itself grows from the user generated content.
More generally, the concept of using games is one of the many technique that full under the general computer trend of crowdsourcing or the games are referred to Games With a Purpose (GWAP). An existing example of GWAP is image labeling to provide a corpus of user generated tag information for static images. There is no prior technique that uses the power of GWAP technologies to improve relevance of search results. Thus there is need in the art of a system that leverages data generated by crowdsourcing and/or GWAP to improve the performance of algorithmic search ranking system and also provide scalability in gathering large amount of user preference data.